Targeting Revenue Leaders for a New Product

Historically, when targeting potential adopters of a new product, firms have tended to focus first on people with disproportional effect on others, often labeled opinion leaders. The idea is that getting to opinion leaders early will help to accelerate the overall adoption process in the population. If one defines social value as the long-term monetary value that a person creates by affecting others, then targeting opinion leaders should benefit the firm through the creation of much social value.

Yet what if information on the connectivity of customers is not available and opinion leaders cannot be identified? In this report authors Haenlein and Libai suggest an appealing alternative: target the expected high profit customers or revenue leaders. Those customers should be not only profitable by their own purchases but also have higher-than-random social value; not necessarily because they affect many others but because they may affect the right others. This idea is based on network assortativity, a prevalent finding in social network analysis whereby people’s social networks tend to be composed of others who are similar to themselves. This similarity should be reflected in consumption patterns, and so revenue leaders should have, on average, friends who are higher-than-average potential themselves, which will create a higher social value.

To support their claim, Haenlein and Libai combine empirical data from a European cellular provider with an agent-based model, a would-be-world simulation technique that enables researchers to conduct simulated experiments in a complex network environment. They find that when launching a new product, revenue leader targeting may be an attractive alternative.

While the social value of revenue leaders is in between that of random customers and opinion leaders, because early adoption of revenue leaders brings its own high value through large cash streams acceleration, the total value created by revenue leader targeting may be even larger than that of opinion leader targeting.

The overall value created by targeting early on opinion leaders, revenue leaders, and random customers is affected by factors such as the size of the target (seed size) and the distribution of profitability in the population. For example, in markets where customer lifetime value is more concentrated, the total value of revenue leader seeding will be higher due to both direct and social value. Further, geographical analysis can provide indications of inter-category variance in profitability, which can be a helpful resource for managers seeking to assess the customer lifetime value variation for a new product.

Acknowledgments

The authors thank Gil Appel, Irit Nitzan, and Gal Ostreicher-Singer for helpful comments and discussion, Matthew Jackson for his help in programming setup, and Applied Geographic Solutions for their help and generosity with their data. This research was partially supported by Marketing Science Institute Research Grant No. 4-1726 and the Israel Science Foundation.